code for paper "Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?"

Overview

Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?

Code for paper:

Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?
Shen Yan, Yu Zheng, Wei Ao, Xiao Zeng, Mi Zhang.
NeurIPS 2020.

arch2vec
Top: The supervision signal for representation learning comes from the accuracies of architectures selected by the search strategies. Bottom (ours): Disentangling architecture representation learning and architecture search through unsupervised pre-training.

The repository is built upon pytorch_geometric, pybnn, nas_benchmarks, bananas.

1. Requirements

  • NVIDIA GPU, Linux, Python3
pip install -r requirements.txt

2. Experiments on NAS-Bench-101

Dataset preparation on NAS-Bench-101

Install nasbench and download nasbench_only108.tfrecord under ./data folder.

python preprocessing/gen_json.py

Data will be saved in ./data/data.json.

Pretraining

bash models/pretraining_nasbench101.sh

The pretrained model will be saved in ./pretrained/dim-16/.

arch2vec extraction

bash run_scripts/extract_arch2vec.sh

The extracted arch2vec will be saved in ./pretrained/dim-16/.

Alternatively, you can download the pretrained arch2vec on NAS-Bench-101.

Run experiments of RL search on NAS-Bench-101

bash run_scripts/run_reinforce_supervised.sh 
bash run_scripts/run_reinforce_arch2vec.sh 

Search results will be saved in ./saved_logs/rl/dim16

Generate json file:

python plot_scripts/plot_reinforce_search_arch2vec.py 

Run experiments of BO search on NAS-Bench-101

bash run_scripts/run_dngo_supervised.sh 
bash run_scripts/run_dngo_arch2vec.sh 

Search results will be saved in ./saved_logs/bo/dim16.

Generate json file:

python plot_scripts/plot_dngo_search_arch2vec.py

Plot NAS comparison curve on NAS-Bench-101:

python plot_scipts/plot_nasbench101_comparison.py

Plot CDF comparison curve on NAS-Bench-101:

Download the search results from search_logs.

python plot_scripts/plot_cdf.py

3. Experiments on NAS-Bench-201

Dataset preparation

Download the NAS-Bench-201-v1_0-e61699.pth under ./data folder.

python preprocessing/nasbench201_json.py

Data corresponding to the three datasets in NAS-Bench-201 will be saved in folder ./data/ as cifar10_valid_converged.json, cifar100.json, ImageNet16_120.json.

Pretraining

bash models/pretraining_nasbench201.sh

The pretrained model will be saved in ./pretrained/dim-16/.

Note that the pretrained model is shared across the 3 datasets in NAS-Bench-201.

arch2vec extraction

bash run_scripts/extract_arch2vec_nasbench201.sh

The extracted arch2vec will be saved in ./pretrained/dim-16/ as cifar10_valid_converged-arch2vec.pt, cifar100-arch2vec.pt and ImageNet16_120-arch2vec.pt.

Alternatively, you can download the pretrained arch2vec on NAS-Bench-201.

Run experiments of RL search on NAS-Bench-201

CIFAR-10: ./run_scripts/run_reinforce_arch2vec_nasbench201_cifar10_valid.sh
CIFAR-100: ./run_scripts/run_reinforce_arch2vec_nasbench201_cifar100.sh
ImageNet-16-120: ./run_scripts/run_reinforce_arch2vec_nasbench201_ImageNet.sh

Run experiments of BO search on NAS-Bench-201

CIFAR-10: ./run_scripts/run_bo_arch2vec_nasbench201_cifar10_valid.sh
CIFAR-100: ./run_scripts/run_bo_arch2vec_nasbench201_cifar100.sh
ImageNet-16-120: ./run_scripts/run_bo_arch2vec_nasbench201_ImageNet.sh

Summarize search result on NAS-Bench-201

python ./plot_scripts/summarize_nasbench201.py

The corresponding table will be printed to the console.

4. Experiments on DARTS Search Space

CIFAR-10 can be automatically downloaded by torchvision, ImageNet needs to be manually downloaded (preferably to a SSD) from http://image-net.org/download.

Random sampling 600,000 isomorphic graphs in DARTS space

python preprocessing/gen_isomorphism_graphs.py

Data will be saved in ./data/data_darts_counter600000.json.

Alternatively, you can download the extracted data_darts_counter600000.json.

Pretraining

bash models/pretraining_darts.sh

The pretrained model is saved in ./pretrained/dim-16/.

arch2vec extraction

bash run_scripts/extract_arch2vec_darts.sh

The extracted arch2vec will be saved in ./pretrained/dim-16/arch2vec-darts.pt.

Alternatively, you can download the pretrained arch2vec on DARTS search space.

Run experiments of RL search on DARTS search space

bash run_scripts/run_reinforce_arch2vec_darts.sh

logs will be saved in ./darts-rl/.

Final search result will be saved in ./saved_logs/rl/dim16.

Run experiments of BO search on DARTS search space

bash run_scripts/run_bo_arch2vec_darts.sh

logs will be saved in ./darts-bo/ .

Final search result will be saved in ./saved_logs/bo/dim16.

Evaluate the learned cell on DARTS Search Space on CIFAR-10

python darts/cnn/train.py --auxiliary --cutout --arch arch2vec_rl --seed 1
python darts/cnn/train.py --auxiliary --cutout --arch arch2vec_bo --seed 1
  • Expected results (RL): 2.60% test error with 3.3M model params.
  • Expected results (BO): 2.48% test error with 3.6M model params.

Transfer learning on ImageNet

python darts/cnn/train_imagenet.py  --arch arch2vec_rl --seed 1 
python darts/cnn/train_imagenet.py  --arch arch2vec_bo --seed 1
  • Expected results (RL): 25.8% test error with 4.8M model params and 533M mult-adds.
  • Expected results (RL): 25.5% test error with 5.2M model params and 580M mult-adds.

Visualize the learned cell

python darts/cnn/visualize.py arch2vec_rl
python darts/cnn/visualize.py arch2vec_bo

5. Analyzing the results

Visualize a sequence of decoded cells from the latent space

Download pretrained supervised embeddings of nasbench101 and nasbench201.

bash plot_scripts/drawfig5-nas101.sh # visualization on nasbench-101
bash plot_scripts/drawfig5-nas201.sh # visualization on nasbench-201
bash plot_scripts/drawfig5-darts.sh  # visualization on darts

The plots will be saved in ./graphvisualization.

Plot distribution of L2 distance by edit distance

Install nas_benchmarks and download nasbench_full.tfrecord under the same directory.

python plot_scripts/distance_comparison_fig3.py

Latent space 2D visualization

bash plot_scripts/drawfig4.sh

the plots will be saved in ./density.

Predictive performance comparison

Download predicted_accuracy under saved_logs/.

python plot_scripts/pearson_plot_fig2.py

Citation

If you find this useful for your work, please consider citing:

@InProceedings{yan2020arch,
  title = {Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?},
  author = {Yan, Shen and Zheng, Yu and Ao, Wei and Zeng, Xiao and Zhang, Mi},
  booktitle = {NeurIPS},
  year = {2020}
}
Zero-Cost Proxies for Lightweight NAS

Zero-Cost-NAS Companion code for the ICLR2021 paper: Zero-Cost Proxies for Lightweight NAS tl;dr A single minibatch of data is used to score neural ne

SamsungLabs 108 Dec 20, 2022
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding This repo contains the data and source code for baseline models in the NeurIPS 2

Microsoft 29 Dec 29, 2022
Modification of convolutional neural net "UNET" for image segmentation in Keras framework

ZF_UNET_224 Pretrained Model Modification of convolutional neural net "UNET" for image segmentation in Keras framework Requirements Python 3.*, Keras

209 Nov 02, 2022
Use .csv files to record, play and evaluate motion capture data.

Purpose These scripts allow you to record mocap data to, and play from .csv files. This approach facilitates parsing of body movement data in statisti

21 Dec 12, 2022
Deep generative models of 3D grids for structure-based drug discovery

What is liGAN? liGAN is a research codebase for training and evaluating deep generative models for de novo drug design based on 3D atomic density grid

Matt Ragoza 152 Jan 03, 2023
Employee-Managment - Company employee registration software in the face recognition system

Employee-Managment Company employee registration software in the face recognitio

Alireza Kiaeipour 7 Jul 10, 2022
Jremesh-tools - Blender addon for quad remeshing

JRemesh Tools Blender 2.8 - 3.x addon for quad remeshing. Currently it is a wrap

Jayanam 89 Dec 30, 2022
Code for ICCV 2021 paper "Distilling Holistic Knowledge with Graph Neural Networks"

HKD Code for ICCV 2021 paper "Distilling Holistic Knowledge with Graph Neural Networks" cifia-100 result The implementation of compared methods are ba

Wang Yucheng 30 Dec 18, 2022
It is an open dataset for object detection in remote sensing images.

RSOD-Dataset It is an open dataset for object detection in remote sensing images. The dataset includes aircraft, oiltank, playground and overpass. The

136 Dec 08, 2022
Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

Official repository of OFA. Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework

OFA Sys 1.4k Jan 08, 2023
Drone Task1 - Drone Task1 With Python

Drone_Task1 Matching Results 3.mp4 1.mp4

MLV Lab (Machine Learning and Vision Lab at Korea University) 11 Nov 14, 2022
TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A good teacher is patient and consistent by Beyer et al.

FunMatch-Distillation TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A g

Sayak Paul 67 Dec 20, 2022
[CVPR 2022] Pytorch implementation of "Templates for 3D Object Pose Estimation Revisited: Generalization to New objects and Robustness to Occlusions" paper

template-pose Pytorch implementation of "Templates for 3D Object Pose Estimation Revisited: Generalization to New objects and Robustness to Occlusions

Van Nguyen Nguyen 92 Dec 28, 2022
Code for 1st place solution in Sleep AI Challenge SNU Hospital

Sleep AI Challenge SNU Hospital 2021 Code for 1st place solution for Sleep AI Challenge (Note that the code is not fully organized) Refer to the notio

Saewon Yang 13 Jan 03, 2022
Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations

Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations Requirements The code is implemented in Python and requires

1 Nov 03, 2021
A video scene detection algorithm is designed to detect a variety of different scenes within a video

Scene-Change-Detection - A video scene detection algorithm is designed to detect a variety of different scenes within a video. There is a very simple definition for a scene: It is a series of logical

1 Jan 04, 2022
NUANCED is a user-centric conversational recommendation dataset that contains 5.1k annotated dialogues and 26k high-quality user turns.

NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions Overview NUANCED is a user-centric conversational recommen

Facebook Research 18 Dec 28, 2021
A minimal implementation of Gaussian process regression in PyTorch

pytorch-minimal-gaussian-process In search of truth, simplicity is needed. There exist heavy-weighted libraries, but as you know, we need to go bare b

Sangwoong Yoon 38 Nov 25, 2022
Official repo for AutoInt: Automatic Integration for Fast Neural Volume Rendering in CVPR 2021

AutoInt: Automatic Integration for Fast Neural Volume Rendering CVPR 2021 Project Page | Video | Paper PyTorch implementation of automatic integration

Stanford Computational Imaging Lab 149 Dec 22, 2022
An introduction to bioimage analysis - http://bioimagebook.github.io

Introduction to Bioimage Analysis This book tries explain the main ideas of image analysis in a practical and engaging way. It's written primarily for

Bioimage Book 20 Nov 28, 2022